Browsing by Author "Watit Benjapolakul"

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    Brain Cancer Tumor Detection by U-Net Deep Learning Algorithm from MRI Images
    (Institute of Electrical and Electronics Engineers Inc., 2024) Utpal Chandra Das; Watit Benjapolakul; Manoj Gupta; Timporn Vitoonpong; Pannee Suanpang; Chanyanan Somthawinpongsai; Sujin Butdisuwan; Aziz Nanthaamornphong; U.C. Das; Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand; email: dasutpal999@gmail.com
    This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.
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    Development of automatic CNC machine with versatile applications in art, design, and engineering
    (Elsevier B.V., 2024) Utpal Chandra Das; Nagoor Basha Shaik; Pannee Suanpang; Rajib Chandra Nath; Kedar Mallik Mantrala; Watit Benjapolakul; Manoj Gupta; Chanyanan Somthawinpongsai; Aziz Nanthaamornphong; U.C. Das; Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Faculty of Engineering, Chulalongkorn University, Bangkok, Pathum Wan District, 10330, Thailand; email: utpal597@gmail.com; A. Nanthaamornphong; College of Computing, Prince of Songkla University, Phuket, Phuket Campus, Thailand; email: aziz.n@phuket.psu.ac.th
    The area of computer numerical control (CNC) machines has grown fast, and their use has risen significantly in recent years. This article presents the design and development of a CNC writing machine that uses an Arduino, a motor driver, a stepper motor, and a servo motor. The machine is meant to create 2D designs and write in numerous input languages using 3-axis simultaneous interpolated operations. The suggested machine is low-cost, simple to build, and can be operated with merely G codes. The performance of the CNC writing machine was assessed by testing it on a range of solid surfaces, including paper, cardboard, and wood. The results reveal that the machine can generate high-quality text and images with great accuracy and consistency. The proposed machine's ability to write in several input languages makes it appropriate for various applications, including art, design, and engineering. © 2024 The Author(s)